Multibeam radar system based on waveform diversity for RF seeker applications

Existing radiofrequency (RF) seekers use mechanically steerable antennas. In order to improve the robustness and performance of the missile seeker, current research is investigating the replacement of mechanical 2D antennas with active electronically controlled 3D antenna arrays capable of steering much faster and more accurately than existing solutions. 3D antenna arrays provide increased radar coverage, as a result of the conformal shape and flexible beam steering in all directions. Therefore, additional degrees of freedom can be exploited to develop a multifunctional seeker, a very sophisticated sensor that can perform multiple simultaneous tasks and meet spectral allocation requirements. This thesis presents a novel radar configuration, named multibeam radar (MBR), to generate multiple beams in transmission by means of waveform diversity. MBR systems based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver with digital signal processing. The advantage is that MBR transmit differently designed waveforms in arbitrary directions so that waveforms can be selected to provide multiple radar functions and better manage the available resources. An analytical model of an MBR is derived to analyse the relationship between individual channels and their performance in terms of isolation and phase steering effects. Combinations of linear frequency modulated (LFM) waveforms are investigated and the analytical expressions of the isolation between adjacent channels are presented for rectangular and Gaussian amplitude modulated LFM signals with different bandwidths, slopes and frequency offsets. The theoretical results have been tested experimentally to corroborate the isolation properties of the proposed waveforms. In addition, the practical feasibility of the MBR concept has been proved with a radar test bed with two orthogonal channels simultaneously detecting a moving target

メタヒューリスティックアルゴリズムにおける成功強度に基づくカオス的局所探索

富山大学・富理工博甲第198号・楊琳・2022/3/23富山大学202

進化的アルゴリズムにおける集団構造の研究

富山大学・富理工博甲第171号・王藝叡・2020/3/24富山大学202

Methodology of synthesis and signal processing of generalized binary Barker sequences for spread spectrum communications

Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.12.02 – «Телекомунікаційні системи та мережі». – Національний авіаційний університет, Київ, 2019. У дисертаційній роботі вирішується актуальна науково-технічна проблема синтезу бінарних дискретно-кодованих послідовностей (ДКП), які є оптимальними за мінімаксним критерієм щодо їх автокореляційної функції (АКФ), у частині синтезу регулярних структур цих ДКП та їх комбінаторних систем в умовах обмежень на максимальний рівень абсолютних значень бічних пелюсток їх АКФ (ДКП Баркера). Вирішенням зазначеної проблеми у дисертації є новий синтезований тип ДКП – узагальнені бінарні послідовності Баркера (УБПБ), які характеризуються регулярними структурами, можуть бути синтезовані регулярними методами синтезу та утворюють нові мультиплікативно комплементарні структури бінарних ДКП. У роботі розроблено методологію синтезу та обробки УБПБ та їх мультиплікативно комплементарних структур, яка у своєму складі містить розроблену параметрично-критеріальну модифікацію EM-алгоритму з видаленням компонент гаусівської змішаної моделі для аналізу кореляційних зв’язків у системах ДКП та доведені теореми про його математичну сингулярність за певних умов такого статистичного аналізу для обґрунтування введених у модифікації алгоритму критеріїв, розроблені метод структуризації ДКП з апріорі невідомими внутрішніми структурами, регулярний метод синтезу УБПБ, метод синтезу та сумісної обробки мультиплікативно комплементарних структур УБПБ, метод декомпозиції структури вихідного сигналу системи обробки мультиплікативно комплементарних УБПБ, метод39 оцінювання енергетичних параметрів ортогональних сигнально-кодових конструкцій та завад при передаванні УБПБ. У дослідженні також обґрунтовано класифікацію УБПБ, виявлено та досліджено системні властивості регулярних структур УБПБ та їх АКФ, синтезовано повну систему математичних моделей для аналітичного опису АКФ УБПБ, розроблено аналітичні моделі оцінювання показників якості передавання повідомлень у телекомунікаційних системах при використанні УБПБ.Диссертация на соискание учёной степени доктора технических наук по специальности 05.12.02 – «Телекоммуникационные системы и сети». – Национальный авиационный университет, Киев, 2019. Диссертационная работа посвящена решению актуальной научно-технической проблемы синтеза бинарных дискретно-кодированных последовательностей (ДКП), оптимальных по минимаксному критерию в отношении их автокорреляционной функции (АКФ), в части синтеза регулярных структур этих ДКП и их комбинаторных систем в условиях ограничений на максимальный уровень абсолютных значений боковых лепестков их АКФ (ДКП Баркера). Решением указанной проблемы в диссертации является новый синтезированный тип ДКП – обобщённые бинарные последовательности Баркера (ОБПБ), которые характеризуются регулярными структурами, могут быть синтезированы регулярными методами синтеза и образовывают новые мультипликативно комплементарные структуры бинарных ДКП. В работе разработана методология синтеза и обработки ОБПБ и их мультипликативно комплементарных структур, которая в своём составе содержит разработанную параметрически-критериальную модификацию EM-алгоритма с удалением компонент гауссовской смешанной модели для анализа корреляционных связей в системах ДКП и доказанные теоремы о его математической сингулярности в определённых условиях такого статистического анализа для обоснования введённых в модификации алгоритма критериев, разработанные метод структуризации ДКП с априори неизвестными внутренними структурами, регулярный метод синтеза ОБПБ, метод синтезу и совместной обработки мультипликативно комплементарных структур ОБПБ, метод декомпозиции структуры сигнала на выходе системы обработки мультипликативно комплементарных ОБПБ, метод оценивания энергетических параметров ортогональных сигнально-кодовых конструкций и помех при передаче ОБПБ. В исследовании также обоснована классификация ОБПБ, выявлены и исследованы системные свойства регулярных структур ОБПБ и их АКФ, синтезирована полная система математических моделей для аналитического описания АКФ ОБПБ, разработаны аналитические модели оценивания показателей качества передачи сообщений в телекоммуникационных системах при использовании ОБПБ.Thesis for a degree of Doctor of Technical Science in specialty 05.12.02 – «Telecommunication Systems and Networks». – National Aviation University. – Kyiv, 2019. The thesis is devoted to solving the actual scientific and engineering problem dealing with a synthesis of binary sequences, which are optimal by the minimax criterion with respect to their autocorrelation function, in terms of a synthesis of regular structures of these binary sequences and their combinatorial systems under additional restrictions on the peak sidelobe level of their autocorrelation function (Barker sequences). The solution of the problem, proposed in the thesis, boils down to a new synthesized kind of binary sequences – generalized binary Barker sequences, which are characterized by regular structures, can be synthesized by means of regular synthesis method and form new multiplicative complementary structures of binary sequences. The methodology of synthesis and signal processing of generalized binary Barker sequences and their multiplicative complementary structures, developed in the thesis, consists of: (a) the modification (parametric and criteria features) of the expectationmaximization (EM) algorithm with removing components of the Gaussian mixture model and additional clustering criteria for a statistical analysis of cross-correlations between sequences in a system for their further structuring, based on proved theorems on mathematical singularities in the log-likelihood function in the mentioned statistical analysis of cross-correlations; (b) the method of structuring binary sequences with a priori unknown structures, which provides selecting groups of binary sequences with interconnected structures and further detecting these interconnected structures in an explicit form; (c) the regular method for synthesis of generalized binary Barker sequences, based on the deterministic generation rules for these sequences; (d) the method for synthesis and joint signal processing of multiplicative complementary structures of generalized binary Barker sequences, based on the multiplication of results of matched filtering of signal components; (e) the method of a structural decomposition of output signal in signal processing system for multiplicative complementary generalized binary Barker sequences (an output signal can be represented by some number of separately taken partial lobes, each of which is characterized by constant mean value and variance of signal), which allows to perform a statistical analysis of output signal for noise immunity analysis, detection and other purposes in telecommunication system; (f) the method of estimation of energetic parameters of orthogonal signal-code constructions and noise on the physical layer of telecommunication system in case of use of generalized binary Barker sequences. The classification by types and subtypes of generalized binary Barker sequences, based on statistical clustering using the EM and k-means algorithms, is also justified in the research. The properties of regular structures of generalized binary Barker sequences and properties of their autocorrelation functions are detected and studied. A complete system of mathematical models for analytical description of the autocorrelation function of generalized binary Barker sequences is synthesized. The analytical models for estimation of quality characteristics on the physical layer of telecommunication system in case of use of generalized binary Barker sequences are developed. Spectral and detection features of generalized binary Barker sequences and their comparison with Golay complementary sequences are also studied in the research. In contrast with Golay complementary sequences, generalized binary Barker sequences provide larger values of the processing gain in sidelobes (by 4.1 dB for a considered case), which provides less noise in sidelobes and a lower number of errors of the first genus in the case of the use of generalized binary Barker sequences. At the same time, the main disadvantage of generalized binary Barker sequences in comparison with Golay complementary sequences is that the processing gain in the main central lobe is lower (by 8.9 dB for a considered case), which causes more noise in the main lobe and a greater number of errors of the second genus in the case of the use of generalized binary Barker sequences. With this, the compared systems of sequences are characterized by almost the same total bandwidth, and the fact that generalized binary Barker sequences also provide a lower pulse width in the main lobe after signal processing (by 1.5 times), which provides a greater maximum data transfer rate and spectral efficiency on the physical layer of spread-spectrum telecommunication system (up to 1.5 times). The research results were implemented in the production and research activities of the UkSATSE Flight Calibration & Rescue Service (Ukrainian State Air Traffic Services Enterprise «UkSATSE») and educational processes at the Faculty of Air Navigation, Electronics and Telecommunications (National Aviation University, Kyiv)

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Understanding Optimisation Processes with Biologically-Inspired Visualisations

Evolutionary algorithms (EAs) constitute a branch of artificial intelligence utilised to evolve solutions to solve optimisation problems abound in industry and research. EAs often generate many solutions and visualisation has been a primary strategy to display EA solutions, given that visualisation is a multi-domain well-evaluated medium to comprehend extensive data. The endeavour of visualising solutions is inherent with challenges resulting from high dimensional phenomenons and the large number of solutions to display. Recently, scholars have produced methods to mitigate some of these known issues when illustrating solutions. However, one key consideration is that displaying the final subset of solutions exclusively (rather than the whole population) discards most of the informativeness of the search, creating inadequate insight into the black-box EA. There is an unequivocal knowledge gap and requirement for methods which can visualise the whole population of solutions from an optimiser and subjugate the high-dimensional problems and scaling issues to create interpretability of the EA search process. Furthermore, a requirement for explainability in evolutionary computing has been demanded by the evolutionary computing community, which could take the form of visualisations, to support EA comprehension much like the support explainable artificial intelligence has brought to artificial intelligence. In this thesis, we report novel visualisation methods that can be used to visualise large and high-dimensional optimiser populations with the aim of creating greater interpretability during a search. We consider the nascent intersection of visualisation and explainability in evolutionary computing. The potential high informativeness of a visualisation method from an early chapter of this work forms an effective platform to develop an explainability visualisation method, namely the population dynamics plot, to attempt to inject explainability into the inner workings of the search process. We further support the visualisation of populations using machine learning to construct models which can capture the characteristics of an EA search and develop intelligent visualisations which use artificial intelligence to potentially enhance and support visualisation for a more informative search process. The methods developed in this thesis are evaluated both quantitatively and qualitatively. We use multi-feature benchmark problems to show the method’s ability to reveal specific problem characteristics such as disconnected fronts, local optima and bias, as well as potentially creating a better understanding of the problem landscape and optimiser search for evaluating and comparing algorithm performance (we show the visualisation method to be more insightful than conventional metrics like hypervolume alone). One of the most insightful methods developed in this thesis can produce a visualisation requiring less than 1% of the time and memory necessary to produce a visualisation of the same objective space solutions using existing methods. This allows for greater scalability and the use in short compile time applications such as online visualisations. Predicated by an existing visualisation method in this thesis, we then develop and apply an explainability method to a real-world problem and evaluate it to show the method to be highly effective at explaining the search via solutions in the objective spaces, solution lineage and solution variation operators to compactly comprehend, evaluate and communicate the search of an optimiser, although we note the explainability properties are only evaluated against the author’s ability and could be evaluated further in future work with a usability study. The work is then supported by the development of intelligent visualisation models that may allow one to predict solutions in optima (importantly local optima) in unseen problems by using a machine learning model. The results are effective, with some models able to predict and visualise solution optima with a balanced F1 accuracy metric of 96%. The results of this thesis provide a suite of visualisations which aims to provide greater informativeness of the search and scalability than previously existing literature. The work develops one of the first explainability methods aiming to create greater insight into the search space, solution lineage and reproductive operators. The work applies machine learning to potentially enhance EA understanding via visualisation. These models could also be used for a number of applications outside visualisation. Ultimately, the work provides novel methods for all EA stakeholders which aims to support understanding, evaluation and communication of EA processes with visualisation